xgb分类问题
# This script shows you how to make a submission using a few
# useful Python libraries.
# It gets a public leaderboard score of 0.76077.
# Maybe you can tweak it and do better...?
import pandas as pd
import xgboost as xgb
from sklearn.preprocessing import LabelEncoder
import numpy as np
# Load the data
train_df = pd.read_csv('./input/train.csv', header=0)
test_df = pd.read_csv('./input/test.csv', header=0)
# We'll impute missing values using the median for numeric columns and the most
# common value for string columns.
# This is based on some nice code by 'sveitser' at http://stackoverflow.com/a/25562948
from sklearn.base import TransformerMixin
class DataFrameImputer(TransformerMixin):
def fit(self, X, y=None):
self.fill = pd.Series([X[c].value_counts().index[0]
if X[c].dtype == np.dtype('O') else X[c].median() for c in X],
index=X.columns)
return self
def transform(self, X, y=None):
return X.fillna(self.fill)
feature_columns_to_use = ['Pclass','Sex','Age','Fare','Parch']
nonnumeric_columns = ['Sex']
# Join the features from train and test together before imputing missing values,
# in case their distribution is slightly different
big_X = train_df[feature_columns_to_use].append(test_df[feature_columns_to_use])
big_X_imputed = DataFrameImputer().fit_transform(big_X)
# XGBoost doesn't (yet) handle categorical features automatically, so we need to change
# them to columns of integer values.
# See http://scikit-learn.org/stable/modules/preprocessing.html#preprocessing for more
# details and options
le = LabelEncoder()
for feature in nonnumeric_columns:
big_X_imputed[feature] = le.fit_transform(big_X_imputed[feature])
# Prepare the inputs for the model
train_X = big_X_imputed[0:train_df.shape[0]].as_matrix()
test_X = big_X_imputed[train_df.shape[0]:].as_matrix()
train_y = train_df['Survived']
# You can experiment with many other options here, using the same .fit() and .predict()
# methods; see http://scikit-learn.org
# This example uses the current build of XGBoost, from https://github.com/dmlc/xgboost
gbm = xgb.XGBClassifier(max_depth=3, n_estimators=300, learning_rate=0.05).fit(train_X, train_y)
predictions = gbm.predict(test_X)
# Kaggle needs the submission to have a certain format;
# see https://www.kaggle.com/c/titanic-gettingStarted/download/gendermodel.csv
# for an example of what it's supposed to look like.
submission = pd.DataFrame({ 'PassengerId': test_df['PassengerId'],
'Survived': predictions })
submission.to_csv("submission.csv", index=False)
替换
df = df.drop('column_name', 1)
where 1 is the axis number (0 for rows and 1 for columns.)
To delete the column without having to reassign df you can do:
df.drop('column_name', axis=1, inplace=True)
Finally, to drop by column number instead of by column label, try this to delete, e.g. the 1st, 2nd and 4th columns:
df.drop(df.columns[[0, 1, 3]], axis=1) # df.columns is zero-based pd.Index
columns = ['Col1', 'Col2', ...]
df.drop(columns, inplace=True, axis=1)
This will delete one or more columns in-place. Note that inplace=True was added in pandas v0.13 and won't work on older versions, do you'd have to do assign the result back in that case:
df = df.drop(columns, axis=1)
